Host-vehicle risk acquisition device and method

ABSTRACT

A host-vehicle risk acquisition device includes a host-vehicle path acquisition portion that acquires a path of a host-vehicle, and an obstacle path acquisition portion that acquires a plurality of paths of an obstacle existing around the host-vehicle. A collision risk acquisition portion acquires an actual collision risk, which is a collision risk between the host-vehicle and the obstacle when the host-vehicle is in a travel state based on the path of the host-vehicle and the plurality of paths of the obstacle. An offset risk acquisition portion acquires an offset risk, which is a collision risk between the host-vehicle and the obstacle in an offset travel state, which is offset from the travel state of the host-vehicle.

BACKGROUND OF THE INVENTION

1. Field of the Invention

This invention relates to a host-vehicle risk acquisition device and ahost-vehicle risk acquisition method that acquire a degree of collisionrisk between a host-vehicle and an obstacle, such as another vehicle

2. Description of the Related Art

A risk acquisition device is available that detects an obstacle around ahost-vehicle, determines possibility of collision between the obstacleand the host-vehicle, and outputs the possibility of collision as adegree or risk. For example, a collision avoidance device uses such arisk acquisition device. When there is a possibility of collisionbetween the host-vehicle and an obstacle, the collision avoidance devicenotifies the driver of a collision risk, or automatically performsdeceleration control of the host-vehicle to avoid collision. (See, e.g.,Japanese Patent Application Publication 7-104062 (JP-A-7-104062).)

However, the collision avoidance device described in JP-A-104062calculates only one predicted path of the obstacle, when the obstacle isa mobile object, such as another vehicle. Thus, when the host-vehicle oranother vehicle is running on the road where many paths of obstacles areexpected, such as an intersection, it is difficult to calculate thepossibility of collision, and the accuracy of the degree of risk may bedecreased in the case where the degree of risk of the host-vehicle iscalculated based on the possibility of collision.

Further, when the degree of risk of the host-vehicle is calculated, thedegree of risk of the host-vehicle in the current state can becalculated; however, the degree of risk of the host-vehicle in the othertravel states may not be calculated. Thus, the safety degree of thehost-vehicle in the other travel state may not be determined.

SUMMARY OF THE INVENTION

The present invention provides a host-vehicle risk acquisition deviceand method that calculates the degree of risk of the host-vehicleaccurately even when the host-vehicle is running at a location wheremany branching paths are possible to be taken, such as an intersection,and determines the safety degree of the host-vehicle in the travelstates other than the current state.

An first aspect of the present invention provides a host-vehicle riskacquisition device including an host-vehicle path acquisition portionthat acquires a path of an host-vehicle; an obstacle path acquisitionportion that acquires a plurality of paths of an obstacle existingaround the host-vehicle; a collision risk acquisition portion thatacquires an actual collision risk, which is a collision risk between thehost-vehicle and the obstacle when the host-vehicle is in a travel statebased on the path of the host-vehicle and the plurality of paths of theobstacle; and an offset risk acquisition portion that acquires an offsetrisk, which is a collision risk between the host-vehicle and theobstacle in an offset travel state, which is offset from the travelstate of the host-vehicle.

According to the first aspect of the present invention, the actualcollision risk between the host-vehicle and the obstacle is acquiredbased on the host-vehicle path and plural paths of the obstacle.Therefore, a collision risk of the host-vehicle may be calculatedaccurately, even if the vehicle is traveling at a location where manybranching paths are possible to be taken, such as an intersection.Further, the actual collision risk between the host-vehicle and theobstacle when the host-vehicle is in a travel state, and the offsetrisk, which is a risk of collision between the host-vehicle and theobstacle in the offset travel state, are acquired. Thus, by obtainingthe offset risk and comparing with the actual collision risk of thehost-vehicle and the offset risk, the degree of safety of thehost-vehicle with respect to the other travel states may be determined.

The travel state of the host-vehicle may include at least one of aposition and a speed of the host-vehicle. Thus, the position or thespeed of the host-vehicle may be used as the travel state of thehost-vehicle.

Further, host-vehicle risk acquisition device may further includes anoffset risk storing portion that stores the offset risk acquired by theoffset risk acquisition portion; and a travel state evaluation portionthat compares the offset risk stored in the offset risk storing portionwith the actual collision risk obtained by the collision riskacquisition portion to evaluate the travel state of the host-vehicle.

When the offset risk stored in the offset risk storing portion iscompared with the actual collision risk obtained by the collision riskacquisition portion, it may be considered that, if the differencetherebetween is small, the risk degree of the travel state is low, andif the difference therebetween is large, the risk degree of the travelstate is high. Accordingly, by comparing the offset risk stored in theoffset risk storing portion with the actual collision risk obtained bythe collision risk acquisition portion, the travel state in the past maybe evaluated.

Further, the travel state evaluation portion may calculate a timevariation in a difference between the offset risk stored in the offsetrisk storing portion and the actual collision risk obtained by thecollision risk acquisition portion, and may evaluate the travel state ofthe host-vehicle based on the calculated time variation in thedifference between the offset risk and the actual collision risk.

By evaluating the travel state of the host-vehicle based on the timevariation in the difference between the offset risk obtained by theoffset risk acquisition portion and the actual collision risk obtainedby the collision risk acquisition portion, the travel state of thehost-vehicle may be evaluated accurately.

Furthermore, the travel state evaluation portion may evaluate the travelstate of the host-vehicle using a mean value and a variance of the timevariation in the difference between the offset risk and the actualcollision risk

By evaluating the travel state of the host-vehicle by using the meanvalue and the variance of the time variation in the difference betweenthe offset risk and the actual collision risk, the travel states of thehost-vehicle may be evaluated more accurately.

A second aspect of the present invention provides a host-vehicle riskacquisition method. A travel state of a host-vehicle is detected. Aoffset travel state of the host-vehicle, which is offset from thehost-vehicle travel state, is calculated. A host-vehicle offset possiblepath is calculated based on the host-vehicle offset travel state. Aplurality of possible paths of an obstacle existing around thehost-vehicle is calculated. Further, an offset risk, which is acollision risk between the host-vehicle and the obstacle when thehost-vehicle travels on the host-vehicle offset possible path.

BRIEF DESCRIPTION OF THE DRAWINGS

The foregoing and further objects, features and advantages of theinvention will become apparent from the following description of exampleembodiments with reference to the accompanying drawings, wherein likenumerals are used to represent like elements and wherein:

FIG. 1 is a block diagram illustrating a configuration of a host-vehiclerisk acquisition device according to a first embodiment of the presentinvention;

FIG. 2 is a flowchart illustrating an operation of the host-vehicle riskacquisition device according to the first embodiment;

FIG. 3 is a view schematically showing travel states of the host-vehicleand other vehicles;

FIG. 4 is a view schematically showing possible paths that may be takenby a host-vehicle;

FIG. 5 is a graph showing the configuration of a time-space environment;and

FIG. 6 is a block diagram illustrating a configuration of a host-vehiclerisk acquisition device according to a second embodiment of the presentinvention.

DETAILED DESCRIPTION OF EMBODIMENTS

Embodiments of the present invention will be described below withreference to the attached drawings. It should be noted that in thedescription of the drawings, the same reference numerals are used todenote the same elements, and repetitive description is omitted. Also,for the convenience of illustration, dimensional ratios in the drawingsdo not necessarily coincide with those in the description.

FIG. 1 is a block diagram illustrating a configuration of a host-vehiclerisk acquisition ECU according to a first embodiment of the presentinvention. As shown in FIG. 1, a host-vehicle risk acquisition ECU 1,which may be regarded as a host-vehicle risk acquisition device of theclaimed invention, is a computer of an automotive device that iselectronically controlled, and includes a CPU (Central Processing Unit),a ROM (Read Only Memory), a RAM (Random Access Memory), an input/outputinterface, and the like. The host-vehicle risk acquisition ECU 1includes an obstacle possible path calculating portion 11, a travelstate changing portion 12, a host-vehicle possible path calculatingportion 13, an optimal path calculating portion 14, and a travel statesorting portion 15. Further, an obstacle sensor 2 is connected to thehost-vehicle risk acquisition ECU 1 via an obstacle extracting portion3, and also a host-vehicle sensor 4 is connected to the host-vehiclerisk acquisition ECU 1. Further, a display device 5 is connected to thehost-vehicle risk acquisition ECU 1.

The obstacle sensor 2 may include a millimeter wave radar sensor, alaser radar sensor, an image sensor, and the like, and detects obstaclessuch as other vehicles or passersby around the host-vehicle. Theobstacle sensor 2 transmits information related to any detected obstacleto the obstacle extracting portion 3 (hereinafter, referred to as“obstacle-related information”).

The obstacle extracting portion 3 extracts obstacles from theobstacle-related information transmitted from the obstacle sensor 2, andoutputs obstacle information such as the position or speed of theobstacle to the obstacle possible path calculating portion 11 in thehost-vehicle risk acquisition ECU 1. If, for example, the obstaclesensor 2 is a millimeter-wave radar sensor or laser radar sensor, theobstacle extracting portion 3 detects an obstacle based on thewavelength or the like of the wave reflected from the obstacle. If theobstacle sensor 2 is an image sensor, the obstacle extracting portion 3extracts, for example, another vehicle as an obstacle from a capturedimage by pattern matching or other suitable technique.

The host-vehicle sensor 4 may include, for example, a position sensor, aspeed sensor, a yaw rate sensor, and detects a current travel state ofthe host-vehicle. The host-vehicle sensor 4 transmits informationrelated to the detected current travel state of the host-vehicle(hereinafter, referred to as “host-vehicle travel state information”) tothe travel state changing portion 12 of the host-vehicle riskacquisition ECU 1. The travel state information of the host-vehicle atthis time may include, for example, the position, speed or yaw rate ofthe host-vehicle.

The obstacle possible path calculating portion 11 stores pluralbehaviors of each obstacle assumed to be taken during a fixed time, andcalculates plural paths of an obstacle that are predicted based on theobstacle information output from the obstacle extracting portion 3 andthe stored behaviors. The obstacle possible path calculating portion 11outputs the information related to the paths of the obstacle to theoptimal path calculating portion 14 (hereinafter, referred to as the“obstacle path information”).

The travel state changing portion 12 calculates offset travel states ofthe host-vehicle. Each offset travel state of the host-vehicle, which isoffset from the current travel state, is calculated based on thehost-vehicle travel state information transmitted from the host-vehiclesensor 4 by adding a small offset value (amount of displacement) to thecurrent travel state. Here, offset of the travel state is realized byoffsetting or changing the position, speed or direction of thehost-vehicle, for example. The travel state changing portion 12 thuschanges the current travel state of the host-vehicle to the pluraloffset travel states of the host-vehicle. Further, the travel statechanging portion 12 outputs offset travel state information includingthe current travel state and the offset travel states of thehost-vehicle to the host-vehicle possible path calculating portion 13and the travel state sorting portion 15.

The host-vehicle possible path calculating portion 13 calculateshost-vehicle possible paths based on the current travel state and theoffset travel states of the host-vehicle in the host-vehicle offsettravel state information output from the travel state changing portion12. Here, N-possible paths are calculated for each of the current travelstate and the offset travel states of the host-vehicle. The host-vehiclepossible path calculating portion 13 sends host-vehicle possible pathinformation including the calculated host-vehicle possible paths to theoptimal path calculating portion 14.

The optimal path calculating portion 14 calculates optimal paths basedon the obstacle path information output from the obstacle possible pathcalculating portion 11 and the host-vehicle possible path informationoutput from the host-vehicle possible path calculating portion 13. Eachoptimal path is a path of the host-vehicle with the minimum collisionprobability between the host-vehicle and other vehicles among thepossible paths calculated based on one of the current travel state andoffset travel states of the host-vehicle. The optimal path calculatingportion 14 outputs optimal collision probability information to thetravel state sorting portion 15. The optimal collision probabilityinformation includes the optimal path collision probability, which is acollision probability of the calculated optimal path of thehost-vehicle.

The travel state sorting portion 15 searches for or obtains a preferabletravel state of the host-vehicle and obtains a collision probabilityindicating the degree of risk of the host-vehicle based on thehost-vehicle offset travel state information output from the travelstate change portion 12 and the optimal collision probabilityinformation output from the optimal path calculating portion 14. Here, adegree of current collision risk and degrees of offset risk are obtainedas the collision probability. The degree of current collision risk is acollision probability between the host-vehicle and other vehicles on thehost-vehicle possible paths calculated based on the current travel stateof the host vehicle. Each of the degrees of offset risk is a collisionprobability between the host-vehicle and other vehicles on thehost-vehicle possible paths calculated based on one of the plural offsettravel states of the host-vehicle. The travel state sorting portion 15outputs host-vehicle preferable travel state information based on theobtained preferable travel state of the host-vehicle to the displaydevice 5. The travel state sorting portion 15 may be regarded as both ofthe collision risk acquisition portion and the offset risk acquisitionportion of the claimed invention.

The display device 5 includes, for example, a liquid crystal display ora display on a front glass. The display device 5 highlights thepreferable travel state of the host-vehicle based on the host-vehiclepreferable travel state information output from the travel state sortingportion 15. Here, the highlight may be realized by changing color,increasing brightness, or thickening lines, as compared with otherdisplayed objects.

An operation of the host-vehicle risk acquisition device of theembodiment having the above described configuration will be describedbelow. FIG. 2 is a flowchart illustrating the operation of thehost-vehicle risk acquisition device. As shown in FIG. 2, in thehost-vehicle risk acquisition device, the obstacle extracting portion 3extracts obstacles around the host-vehicle based on the obstacle-relatedinformation transmitted from the obstacle sensor 2 (S1). At this time,another vehicle is extracted as the obstacle. If other vehicles areincluded in the obstacle-related information, the obstacle extractingportion 3 extracts all of the other vehicles.

After one of the other vehicles is extracted as the obstacle, theobstacle possible path calculating portion 11 calculates plural possiblepaths along which the other vehicle may move (S2). The possible pathsalong which the other vehicle may move are calculated as trajectories ina time-space defined by time and space for each of the other vehicles.In this case, as the possible paths along which the other vehicle maymove, rather than specifying a given arrival point and then calculatingthe paths to this arrival point, paths in which the other vehicle willmove until a predetermined moving time elapses are obtained. Generally,on the road where the host-vehicle travels, there is no place wheresafety is guaranteed or secured. Hence, even when arrival points for thehost-vehicle and the other vehicle are obtained, in order to determinethe possibility of collision between the host-vehicle and the othervehicle, this does not necessarily ensure reliable collision avoidance.

For example, take the case shown in FIG. 3, where, on a three-lane roadRd, a host-vehicle M is traveling in a first lane r1, a first othervehicle H1 is traveling in a second lane r2, and a second other vehicleH2 is traveling in a third lane r3. At this time, in order to avoidcollision between the host-vehicle M and the other vehicles H1, H2respectively traveling in the second and third lines r2, r3, it would beappropriate for the host-vehicle M to move to positions Q1, Q2, Q3.However, if the second other vehicle H2 takes a path B3 so as to changeto the second lane r2, the first other vehicle H1 will presumably take apath B2 to avoid collision with the second other vehicle H2 and thuswill enter the first lane r1. In this case, if the host-vehicle Marrives at the positions Q1, Q2, Q3, there is a danger of collision withthe first other vehicle H1.

Accordingly, rather than setting arrival positions with respect to thehost-vehicle and the other vehicle in advance, the paths of thehost-vehicle and other vehicle are predicted as necessary. By predictingthe paths of the host-vehicle and other vehicles as needed, it ispossible to properly avoid danger to the host-vehicle M during traveland ensure safety by taking a path B1, shown in FIG. 4, as the path ofthe host-vehicle.

While in the above-described prediction, possible paths along which theother vehicle will move until a predetermined moving time elapses arespecified, alternatively, possible paths of the other vehicle until thetravel distance traveled by the other vehicle reaches a predetermineddistance may be obtained. In this case, the predetermined distance maybe changed as appropriate in accordance with the speed of the othervehicle (or the speed of the host-vehicle).

Possible paths for other vehicles are calculated as follows for eachsuch other vehicle. An initialization process is performed whereby thevalue of a counter k for identifying another vehicle is set to 1, andthe value of a counter n indicating the number of times a possible pathis generated with respect to the same other vehicle is set to 1.Subsequently, the position and moving state (speed and moving direction)of the other vehicle based on other vehicle information (obstacleinformation) transmitted from the obstacle sensor 2 and extracted fromother vehicle related information (obstacle-related information) are setto the initial state.

Subsequently, from among a plurality of behaviors that may be selectedas behaviors of the other vehicle assumed to be taken during a fixedtime Δt after the initialization, one behavior is selected in accordancewith a behavior selection probability assigned to each behavior. Thebehavior selection probability with which one behavior is selected isdefined by, for example, associating elements of a set of behaviors thatmay be selected with predetermined random numbers. In this sense,different behavior selection probabilities may be assigned to individualbehaviors, or an equal probability may be assigned to all the elementsof a set of behaviors. Also, the behavior selection probability may bemade dependent on the position and travel state of the other vehicle orthe surrounding road condition.

Such selection of a behavior of the other vehicle assumed to be takenduring the fixed time Δt based on the behavior selection probability isrepeated, and a behavior of the other vehicle taken until the elapse ofa predetermined time over which the other vehicle moves is selected. Onepossible path for the other vehicle is calculated based on the behaviorof the other vehicle thus selected.

Upon calculating one possible path for the other vehicle, a plurality of(N) possible paths for the other vehicle are calculated through the sameprocedure. Even when the same procedure is employed, because onebehavior is selected in accordance with a behavior selection probabilityassigned to each behavior, different possible paths are calculated inmost cases. The number of possible paths calculated at this time may bedetermined in advance as, for example, 1000 (N=1000). Of course, thenumber of the plurality of possible paths calculated may be different,for example, between several hundreds and several tens of thousand. Thepossible paths thus calculated are set as the predicted paths of theother vehicle.

If a plurality of other vehicles has been extracted, possible paths arecalculated for each of the plurality of other vehicles.

Once the calculation of the possible paths of the other vehicle iscompleted, the travel state changing portion 12 calculates the offsettravel state of the host-vehicle, which is offset from the currenttravel state of the host-vehicle (S3). The offset of the travel state ofthe host-vehicle is performed by slightly changing the position, speed,yaw rate or the like of the host-vehicle transmitted from thehost-vehicle sensor 4. Thus, an offset position, offset speed, offsetyaw rate or the like of the host-vehicle is calculated by slightlychanging the current position, speed, yaw rate or the like of thehost-vehicle. At this time, a plurality of offset travel states of thehost-vehicle is calculated.

Once the offset travel states of the host-vehicle are calculated,possible paths of the host-vehicle are calculated (S4). The host-vehiclepossible paths are calculated based on the host-vehicle offset travelstate information output from the travel state changing portion 12.

Each possible path for the host-vehicle is calculated based on thebehavior of the host-vehicle that is assumed to be taken during thefixed time Δt, from the current travel state and the offset travel stateof the host-vehicle in the host-vehicle offset travel state informationoutput from the travel state changing portion 12. The host-vehiclepossible paths are calculated for each of the current travel state andthe plurality of offset travel states of the host-vehicle. The behaviorof the host-vehicle that is assumed to be taken during the fixed time Δtis obtained by using a behavior selection probability assigned to eachof a plurality of behaviors that are assumed to be taken by the currenttravel state and host-vehicle, relative to the offset travel state ofthe host-vehicle.

For example, the behavior selection probability is set such that if thehost-vehicle offset travel state indicates that the host-vehicle istraveling at high speed, a behavior in which the distance traveled bythe host-vehicle becomes large is likely to be selected, and if the yawrate has gone to either the left or right, a behavior in which thehost-vehicle faces in that direction is likely to be selected. Byselecting a behavior by using a speed or yaw rate as the offset travelstate of the host-vehicle, the path of the host-vehicle may beaccurately predicted. Alternatively, a vehicle speed and estimated curveradius may be obtained based on the host-vehicle offset travel stateinformation output from the travel state changing portion 12, and thepossible path of the host-vehicle may be calculated from the vehiclespeed and the estimated curve radius. The behavior selectionprobabilities for all the behavior may be the same.

After the host-vehicle possible paths are calculated, the optimal pathcalculating portion 14 calculates a collision probability between thehost-vehicle and the other vehicle for each possible path calculatedbased on the current travel state of the host-vehicle, and a collisionprobability between the host-vehicle and the other vehicle for eachpossible path calculated based on each of the plural offset travelstates of the host-vehicle (S5). Now, focus on a single offset travelstate of the host vehicle. An example of the possible paths for theother vehicle calculated in step 2 and the possible paths for thehost-vehicle calculated in step S4 based on the single offset travelstate of the host-vehicle is represented by a three-dimensional spaceshown in FIG. 5. In the three-dimensional space shown in FIG. 5, theposition of a vehicle is represented on an x-y plane defined by anx-axis and a y-axis, with a t-axis set as the time axis. Therefore,possible paths of the other vehicle and possible paths for thehost-vehicle are represented by (x, y, t) coordinates, and trajectoriesobtained by projecting the respective paths of the other vehicle andhost-vehicle onto the x-y plane are the travel trajectories in whichthe, other vehicle and the host-vehicle are predicted to travel.

The possible paths for the other vehicle and the host-vehicle which arethus calculated are expressed in the space shown in FIG. 5 in this way,thus forming a time-space environment including a set of possible pathsthat can be taken by a plurality of vehicles (the other vehicle and thehost-vehicle) that exist within a predetermined range inthree-dimensional time-space. A time-space environment Env(M, H) shownin FIG. 5 represents a set of possible paths for the host-vehicle M anda set of possible paths for the other vehicle H, and includes a possiblepath set {M(n1)} for the host-vehicle M and a possible path set {H(n1)}for the other vehicle H. More specifically, the time-space environmentEnv(M, H) represents a time-space environment in case where thehost-vehicle M and the other vehicle H are traveling in the +y-axisdirection on a smooth and linear road R such as an expressway. Becausepossible paths are obtained independently for each of the host-vehicle Hand the other vehicle M without taking correlation between thehost-vehicle M and the other vehicle H, the possible passes for thesetwo vehicles may sometimes cross in time-space.

Once the possible paths for each of the host-vehicle M and the othervehicle H are obtained, the collision probability between thehost-vehicle M and the other vehicle H is determined. If a possible pathfor the host-vehicle M and a possible path for the other vehicle cross,this means that a collision will occur between the host-vehicle M andthe other vehicle H. In this regard, a possible path for each of thehost-vehicle M and the other vehicle H is determined based on apredetermined behavior selection probability. Therefore, based on thenumber of possible paths that cross a possible path for the host-vehicleM out of the plural possible paths for the other vehicle H, it ispossible to determine the probability of collision between thehost-vehicle M and the other vehicle H. For example, if 1000 possiblepaths for the other vehicle H are calculated, and 5 possible paths outof the 1000 possible paths cross a possible path of the host-vehicle M,the collision probability (collision possibility) PA is calculated to be0.5%. Stated conversely, the remaining 99.5% is the probability of nocollision between the host-vehicle M and the other vehicle H(no-collision probability).

When a plurality of the other vehicles H has been extracted, thecollision probability PA of the host-vehicle with at least one of theplurality of the other vehicles may be obtained by Equation (1) below.

$\begin{matrix}{P_{A} = {1 - {\prod\limits_{i = 1}^{k}\left( {1 - P_{Ai}} \right)}}} & (1)\end{matrix}$

Here, k represents the number of the other vehicles extracted, and PAkrepresents the probability of collision with the k-th vehicle. In thisway, a plurality of possible paths for the other vehicle H arecalculated, and the possibility of collision between the host-vehicle Mand the other vehicle H is calculated by using the plurality of possiblepaths for the other vehicle H, thus calculating a wide range of pathsthat may be taken by the other vehicle. Therefore, a collisionprobability may be calculated by also taking into account cases wherethere is a large change in the path of the other vehicle, such as whenthe vehicles are traveling at a branching location such as anintersection.

The path with the lowest collision probability in the calculated pluralcollision probabilities is determined as an optimal travel path. Then,the collision probability between the host-vehicle and the other vehicleof the optimal travel path is determined as an optimal path collisionprobability of the host-vehicle. The optimal path calculating portion 14calculates, through the same process of step S5, an optimal pathcollision probability between the host-vehicle and the other vehicleamong possible paths calculated based on each of the current travelstate and the plural offset travel states of the host-vehicle.

Once the optimal path collision probabilities between the host-vehicleand the other vehicle are respectively obtained for the plural sets ofpossible paths of the host-vehicle respectively calculated based on thecurrent travel state and the plural offset travel states of thehost-vehicle, the travel state sorting portion 15 determines apreferable travel state of the host-vehicle based on the plural optimalpath collision probabilities for the host-vehicle and the plural offsettravel states of the host-vehicle in the host-vehicle offset travelstate information output from the travel state changing portion 12. Atthis time, the preferable travel state of the host-vehicle is determinedby comparing the optimal path collision probabilities of thehost-vehicle respectively corresponding to the plural offset travelstates of the host-vehicle, and searching for the preferable travelstate for the host-vehicle (S6). The preferable travel state of thehost-vehicle is represented by the offset travel states of thehost-vehicle arranged in the ascending order of the optimal pathcollision probability of the host-vehicle. By searching for or obtainingthe preferable travel state of the host-vehicle in this way, the degreeof risk of the host-vehicle is accurately calculated, and it is possibleto determine which travel state increases the degree of safety.

The travel state sorting portion 15 outputs preferable host-vehicletravel state information including the calculated preferable travelstate of the host-vehicle to the display device 5 (S7). The displaydevice 5 displays travel states of the host-vehicle, such as positionsof the host-vehicle, in the ascending order of the optimal pathcollision probability. By displaying the travel states of thehost-vehicle in the ascending order of the optimal path collisionpossibility, the preferable travel state of the host-vehicle is shown tothe driver.

Next, a second embodiment of the present invention will be described.FIG. 6 is a block diagram illustrating a configuration of a host-vehiclerisk acquisition ECU according to a second embodiment of the presentinvention. As shown in FIG. 6, the host-vehicle risk acquisition ECU 10,which may be regarded as the host-vehicle risk acquisition device of theclaimed invention, is principally different from that of the firstembodiment in regard that a travel state comparing portion 16 isprovided.

In addition, the travel state sorting portion 15 outputs preferablehost-vehicle travel state information including the offset travel statesof the host-vehicle arranged in the ascending order of the optimal pathcollision probability to the travel state comparing portion 16.

The travel state comparing portion 16 includes a travel state storingportion 17 that stores the preferable travel states of the host-vehiclein the preferable host-vehicle travel state information output from thetravel state sorting portion 15. At this time, the preferablehost-vehicle travel state information output from the travel statesorting portion 15 includes an actual travel state of the host-vehicle,which may be calculated from the current travel state of thehost-vehicle.

Subsequently, when the travel state sorting portion 15 outputs thepreferable host-vehicle travel state information, the travel statecomparing portion 16 compares the actual travel state of thehost-vehicle in a current-cycle with the preferable travel state of thehost-vehicle in the previous-cycle. The actual travel state of thehost-vehicle in the current-cycle is included in the preferablehost-vehicle travel state information output from the travel statesorting portion 15. The preferable travel state of the host-vehicle inthe previous-cycle is included in the preferable host-vehicle travelstate information stored in the travel state storing portion.

Based on the result of the comparison, the travel state of thehost-vehicle between when the preferable host-vehicle travel stateinformation is output in the previous-cycle and when the preferablehost-vehicle travel state information is output in the current-cycle isevaluated. Further, the travel state comparing portion 16 outputs thetravel state evaluation information including the evaluation of travelstate to the display device 5. The display device 5 displays theevaluation of travel state included in the travel state evaluationinformation. Other portions are similar to those of the firstembodiment.

An operation of the host-vehicle risk acquisition device according tothe second embodiment having the above-described configuration will beexplained by comparison with the operation in the first embodiment. Theoperation from when obstacles around the host-vehicle are extracted (S1)to when the preferable travel state of the host-vehicle is determined(S6) is similar to that of the first embodiment.

Subsequently, once the preferable travel state of the host-vehicle isobtained, the travel state sorting portion 15 determines outputs thepreferable host-vehicle travel state information including thepreferable travel state of the host-vehicle to the travel statecomparing portion 16. The travel state comparing portion 16 stores thepreferable host-vehicle travel state information output by the travelstate sorting portion 15 into the travel state storing portion 17.

Further, the travel state comparing portion 16 compares the actualtravel state of the host-vehicle with the most preferable travel state(having the minimum optimal collision probability) in a piece of thepreferable host-vehicle travel state information stored in the travelstate storing portion 17 at a certain time point. For example, if theoptimal path collision probability of the actual travel state of thehost-vehicle coincides with the optimal path collision probabilities ofthe most preferable travel states in all pieces of preferablehost-vehicle travel state information respectively stored at some timepoints, it is possible to evaluate that the host-vehicle is in thesafest travel state.

Further, when the pieces of the preferable host-vehicle travel stateinformation stored in the travel state storing portion are arranged inthe order of time from past to present, if the difference between theoptimal path collision probability of the most preferable travel statein the preferable host-vehicle travel state information and the optimalpath collision probability of the actual travel state of thehost-vehicle increases from past to present, it may be evaluated thatthe host-vehicle is gradually apart from a safe state as the timeelapses. On the contrary, it the difference decreases, it is evaluatedthat the host-vehicle is approaching a safer travel state from aninitial travel state that is not the safest state.

In this way, the travel state may be evaluated by focusing on the changein the travel state of the host-vehicle over time.

Further, if a portion that calculates a mean value or variance of thetime variation in the difference between the optimal path collisionprobability in the most preferable travel state and the optimal pathcollision probability of the actual host-vehicle is added to the travelstate comparing portion 16, the stability of safety degree of thehost-vehicle is evaluated by using the mean value and the variance. Forexample, a large variance indicates that the travel state of thehost-vehicle is rapidly and repeatedly changing between safe states anddangerous states. In this case, the travel state is evaluated that thehost-vehicle is in a state in which the behavior is unpredictable byothers. On the other hand, a small variance indicates that the travelstate is either in a consistently safe state or a consistently dangerousstate. In this case, accordingly, although there is difference betweensafety and danger, the travel state is evaluated to be easilypredictable by others.

While some embodiments of the invention have been illustrated above, itis to be understood that the invention is not limited to details of theillustrated embodiments, but may be embodied with various changes,modifications or improvements, which may occur to those skilled in theart, without departing from the spirit and scope of the invention. Forexample, while another vehicle is assumed as an obstacle in theabove-described embodiments, a living being such as a passerby may beassumed as an obstacle.

The invention claimed is:
 1. A host-vehicle risk acquisition devicecomprising: a host-vehicle sensor that detects a travel state of a hostvehicle; a host-vehicle path acquisition portion that acquires a path ofa host-vehicle; an obstacle path acquisition portion that acquires aplurality of paths of an obstacle existing around the host-vehicle; acollision risk acquisition portion that acquires an actual collisionrisk, which is a collision risk between the host-vehicle and theobstacle when the host-vehicle is in a travel state detected by thehost-vehicle sensor based on the path of the host-vehicle and theplurality of paths of the obstacle; and an offset risk acquisitionportion that acquires an offset risk, which is a collision risk betweenthe host-vehicle and the obstacle in an offset travel state, which isobtained by changing the travel state of the host-vehicle detected bythe host-vehicle sensor by adding an offset value to the current travelstate.
 2. The host-vehicle risk acquisition device according to claim 1,wherein the travel state of the host-vehicle includes at least one of aposition and a speed of the host-vehicle.
 3. The host-vehicle riskacquisition device according to claim 1, further comprising: an offsetrisk storing portion that stores the offset risk acquired by the offsetrisk acquisition portion; and a travel state evaluation portion thatcompares the offset risk stored in the offset risk storing portion withthe actual collision risk obtained by the collision risk acquisitionportion to evaluate the travel state of the host-vehicle.
 4. Thehost-vehicle risk acquisition device according to claim 3, wherein thetravel state evaluation portion calculates a time variation in adifference between the offset risk stored in the offset risk storingportion and the actual collision risk obtained by the collision riskacquisition portion, and evaluates the travel state of the host-vehiclebased on the calculated time variation in the difference between theoffset risk and the actual collision risk.
 5. The host-vehicle riskacquisition device according to claim 4, wherein the travel stateevaluation portion evaluates the travel state of the host-vehicle usinga mean value and a variance of the time variation in the differencebetween the offset risk and the actual collision risk.
 6. Thehost-vehicle risk acquisition device according to claim 1, wherein theoffset risk acquisition portion acquires a plurality of offset risksrespectively calculated for a plurality of offset travel states of thehost-vehicle, which are offset from the travel state of the host-vehicleby the amounts different from each other.
 7. The host-vehicleacquisition device according to claim 6, wherein the offset riskacquisition portion determines a most preferable travel state from theplurality of offset travel states based on the plurality of offset risksacquired by the offset risk acquisition portion, the host-vehicle riskacquisition device further comprising a display that displays the mostpreferable travel state.
 8. The host-vehicle risk acquisition deviceaccording to claim 1, further comprising: an offset portion thatcalculates the offset travel state which is offset from the travelstate, wherein the host-vehicle path acquisition portion acquires afirst path that is predicted to be taken by the host-vehicle in thetravel state, and a second path that is predicted to be taken by thehost-vehicle in the offset travel state, wherein the collision riskacquisition portion acquires the actual collision risk based on thefirst path and the plurality of paths of the obstacle, and wherein theoffset risk acquisition portion acquires the offset risk based on thesecond path and the plurality of paths of the obstacle.